The density of expected persistence diagrams and its kernel based estimation

TL;DR

This paper proves that the expected persistence diagram for certain filtrations has a density and introduces a kernel-based method with cross-validation for estimating this density from random data.

Contribution

It establishes the existence of a density for expected persistence diagrams and proposes a kernel estimator with a consistent bandwidth selection method.

Findings

Expected persistence diagrams have a density w.r.t. Lebesgue measure.

Persistence surface can be viewed as a kernel density estimator.

A cross-validation scheme for optimal bandwidth selection is proposed and proven consistent.

Abstract

Persistence diagrams play a fundamental role in Topological Data Analysis where they are used as topological descriptors of filtrations built on top of data. They consist in discrete multisets of points in the plane $\mathbb{R}^2$ that can equivalently be seen as discrete measures in $\mathbb{R}^2$. When the data come as a random point cloud, these discrete measures become random measures whose expectation is studied in this paper. First, we show that for a wide class of filtrations, including the \v{C}ech and Rips-Vietoris filtrations, the expected persistence diagram, that is a deterministic measure on $\mathbb{R}^2$ , has a density with respect to the Lebesgue measure. Second, building on the previous result we show that the persistence surface recently introduced in [Adams & al., Persistence images: a stable vector representation of persistent homology] can be seen as a kernel estimator of this density. We propose a cross-validation scheme for selecting an optimal bandwidth, which is proven to be a consistent procedure to estimate the density.